Xia Wang1, Xing Deng2,3, Hongming Shen1,*, Guodong Zhang1, Shibing Zhang1
CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 693-710, 2021, DOI:10.32604/cmes.2021.013186
Abstract Speech intelligibility enhancement in noisy environments is still one of the major challenges for hearing impaired in everyday life. Recently, Machine-learning based approaches to speech enhancement have shown great promise for improving speech intelligibility. Two key issues of these approaches are acoustic features extracted from noisy signals and classifiers used for supervised learning. In this paper, features are focused. Multi-resolution power-normalized cepstral coefficients (MRPNCC) are proposed as a new feature to enhance the speech intelligibility for hearing impaired. The new feature is constructed by combining four cepstrum at different time–frequency (T–F) resolutions in order to capture both the local and… More >
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